What Is Machine Learning? Definition and Examples
By Chris Pace on November 28, 2017
Artificial intelligence (AI) has truly entered the mainstream consciousness. But how clearly do any of us really understand what AI is? How aware are we of the ways in which we’re interacting with techniques like machine learning, natural language processing, and cognitive analytics every single day?
It’s true that the advanced mathematics and complex programming at the heart of AI systems is challenging for most of us to get our heads around. So here, we’ll focus on understanding what some of these AI techniques (specifically machine learning) do and the difference they can make to our work and lives.
Why Is AI so Tricky to Define?
Webster’s Dictionary defines artificial intelligence as “an area of computer science that deals with giving machines the ability to seem like they have human intelligence.” The fact that this definition is so vague actually very effectively captures the difficulty in grasping what AI really means.
The challenge here is one of perception — measuring human intelligence is controversial enough. Some might say that solving problems, understanding concepts, and recognizing sequences are clear indicators of intelligence. Others would claim that empathy, understanding emotion, and interaction with others are measures of human intellect, not to mention the huge concepts of creativity, imagination, and perception.
To avoid straying into the realms of the metaphysical here, let’s focus instead on how AI is being applied today. Systems based on AI, sometimes referred to as cognitive systems, are helping us automate many tasks which, until recently, were seen as requiring human intelligence. However, AI allows us to not only automate and scale up tasks that so far have required humans, but it also lets us tackle more complex problems than most humans would be capable of solving.
Why Is AI Coming to Prominence Now?
AI has become such a focal point of attention for both researchers and entrepreneurs during the last few years due to several factors contributing to a “perfect storm”:
- Never before has so much information been available in digital form, ready for use. All of humanity is, on a daily basis, providing more information about the world for machines to analyze. Not only that — through crowdsourcing and online communities, we are also able to give feedback on the quality of the machines’ work at an unprecedented scale.
- Computing power and storage capacity continue to grow exponentially, and the cost for accessing these resources in the cloud are decreasing. Incredible resources are now available not only to the world’s largest corporations, but to garage startups as well.
- Research in algorithms has seen huge strides in giving us the ability to use these new computing resources on the massive data sets now available.
How Do Machines Learn?
Machine learning is an AI technique getting significant attention today. The ultimate aim of machine learning is to enable software applications to become more accurate without being explicitly programmed. But how do machines actually learn? The basic premise of machine learning is to build algorithms that can receive vast amounts of data, and then use statistical analysis to provide a reasonably accurate outcome.
Machine-learning algorithms are usually defined as supervised or unsupervised. Supervised algorithms need humans to provide both input and the desired output, in addition to providing the machine with feedback on the outcomes during the training phase. Once training is complete, the algorithm will apply what was learned to new data. Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions.
In reality, machine learning is about setting systems to the task of searching through data to look for patterns and adjusting actions accordingly. For example, Recorded Future is training machines to recognize information such as references to cyberattacks, vulnerabilities, or data breaches. In this case, the machinery isn’t necessarily performing a task that is difficult for a human, but is impossible for a human to perform at the same scale. You can see the capabilities of machines in performing these kinds of task in our man versus machine infographic.
AI Applied to Threat Intelligence
Training machines to process and analyze threat data from numerous sources brings two clear benefits for information security in organizations. Firstly, as previously mentioned, there are significant advantages in the scale of data which can be collected and analyzed by AI systems. This performance gain allows businesses to task people with performing roles that require uniquely human capabilities and will result in greater efficiency. Secondly, the machinery gives structure to the data that makes it infinitely easier to get to relevant threat intelligence quickly.
In our recent webinar “Machine Learning in Black and White,” you can hear more about how the latest AI techniques are being applied in information security by defenders, as well as how attackers are adopting machine learning to conduct increasingly sophisticated attacks and to circumvent AI-based defenses.